Computational Imaging Research Based on Deep Learning

Computational Imaging Using Pixel-level Graph Adversarial Learning

Status

UNKNOWN

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT05471869

Enrollment

1200

Registered

2022-07-25

Start date

2021-11-01

Completion date

2022-08-15

Last updated

2022-07-25

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Arterial Aneurysm

Keywords

Arterial Aneurysm

Brief summary

Computational imaging research based on deep learning

Detailed description

Based on the current technical challenges, subject development and upgrade of knowledge, to avoid the occurrence of adverse medical accidents, simplify the diagnostic process, artificial intelligence has become the alternative method of choice, by constructing training deep learning model, the CTA as model inputs aneurysm detection and diagnosis to improve diagnosis effectiveness, promote the development of medical technology

Interventions

DIAGNOSTIC_TESTAneurysm diagnosis

Intelligent detection and diagnosis of aneurysm diagnosis by CTA

Study design

Observational model

COHORT

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to 80 Years

Healthy volunteers

Yes

Inclusion criteria

1. Age: 18-80 years. 2. CT paired imaging data of vessels, including layer-to-layer plain CT and enhanced CT. 2 Time range: January 2010 to December 2021. 3. Scanning sites: CT and CTA of head, neck, chest, abdomen or iliac.

Exclusion criteria

1. Unpaired CT image . 2. Severe artifact CT image. 3. enhancement CT failure image (failure to capture arterial phase or poor arterial development, insufficient flow of contrast agent, etc.)

Design outcomes

Primary

Measure	Time frame	Description
Objective evaluation	30 minutes	using SSIM\\MAE index evaluation

Countries

China

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 8, 2026